Results 231 to 240 of about 405,852 (280)

Face2Exp: Combating Data Biases for Facial Expression Recognition

Computer Vision and Pattern Recognition, 2022
Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize
Dan Zeng, Zhi-Kai Lin, Xiao Yan, Yuting Liu, Fei Wang, Bo Tang   +5 more
semanticscholar   +1 more source

Facial expression recognition

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2005
The paper aims at recognizing the various human facial expressions. Every countenance is marked by changes in the feature points of the face. These feature points are located in various regions of the face. There are two phases in the facial expression recognition technique described here.
P.K. Manglik, U. Misra, null Prashant, H.B. Maringanti   +3 more
openaire   +1 more source

Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism

IEEE Transactions on Image Processing, 2019
Facial expression recognition in the wild is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially ...
Yong Li, Jiabei Zeng, S. Shan, Xilin Chen   +3 more
semanticscholar   +1 more source

Former-DFER: Dynamic Facial Expression Recognition Transformer

ACM Multimedia, 2021
This paper proposes a dynamic facial expression recognition transformer (Former-DFER) for the in-the-wild scenario. Specifically, the proposed Former-DFER mainly consists of a convolutional spatial transformer (CS-Former) and a temporal transformer (T ...
Zengqun Zhao, Qingshan Liu
semanticscholar   +1 more source

Relation-Aware Facial Expression Recognition

IEEE Transactions on Cognitive and Developmental Systems, 2022
Research on facial expression recognition has been moving from the constrained lab scenarios to the in-the-wild situations and has made progress in recent years.
Yifan Xia, Hui Yu, Xiao Wang, Muwei Jian, Fei-Yue Wang   +4 more
semanticscholar   +1 more source

MMATrans: Muscle Movement Aware Representation Learning for Facial Expression Recognition via Transformers

IEEE Transactions on Industrial Informatics
How to automatically recognize facial expression has caused concerns in industrial human–robot interaction. However, facial expression recognition (FER) is susceptible to problems, such as occlusion, arbitrary orientations, and illumination.
Hai Liu, Qiyun Zhou, Cheng Zhang, Junyan Zhu, Tingting Liu, Zhaoli Zhang, Youfu Li   +6 more
semanticscholar   +1 more source

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition

IEEE Transactions on Image Processing, 2019
Facial expression is central to human experience, but most previous databases and studies are limited to posed facial behavior under controlled conditions.
Shan Li, Weihong Deng
semanticscholar   +1 more source

Facial Expression Recognition with sEMG Method

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015
Facial expression recognition has broad application prospects in the fields of psychological study, nursing care, Human Computer Interaction as well as affective computing. The method with surface Electromyogram (sEMG), which is one of vital bio-signals, has its superiority in several aspects such as high temporal resolution and data processing ...
Tenhunen Aarne Hannu Kristian, Rahmani Amir, Jiang Mingzhe, Liljeberg Pasi Kristian, Westerlund Tomi Vesa Juhani   +4 more
openaire   +2 more sources

Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition

Computer Vision and Pattern Recognition, 2020
Many existing studies reveal that annotation inconsistency widely exists among a variety of facial expression recognition (FER) datasets. The reason might be the subjectivity of human annotators and the ambiguous nature of the expression labels.
Shikai Chen, Jianfeng Wang, Donny Y. Chen, Zhongchao Shi, Xin Geng, Yong Rui   +5 more
semanticscholar   +1 more source

Fine-Grained Facial Expression Recognition in the Wild

IEEE Transactions on Information Forensics and Security, 2021
Over the past decades, researches on facial expression recognition have been restricted within six basic expressions (anger, fear, disgust, happiness, sadness and surprise).
Liqian Liang, Congyan Lang, Yidong Li, Songhe Feng, Jian Zhao   +4 more
semanticscholar   +1 more source